AutoImageProcessor (#20111)

* AutoImageProcessor skeleton

* Update references

* Add mapping in init

* Add model image processors to __init__ for importing

* Add AutoImageProcessor tests

* Fix up

* Image Processor documentation

* Remove pdb

* Update docs/source/en/model_doc/mobilevit.mdx

* Update docs

* Don't add whitespace on json files

* Remove fixtures

* Move checking model config down

* Fix up

* Add check for image processor

* Remove FeatureExtractorMixin in docstrings

* Rename model_tmpfile to config_tmpfile

* Don't make None if not in image processor map

docs/source/en/model_doc/levit.mdx

@@ -19,18 +19,18 @@ The LeViT model was proposed in [LeViT: Introducing Convolutions to Vision Trans
 The abstract from the paper is the following:
 
 *We design a family of image classification architectures that optimize the trade-off between accuracy
-and efficiency in a high-speed regime. Our work exploits recent findings in attention-based architectures, 
-which are competitive on highly parallel processing hardware. We revisit principles from the extensive 
-literature on convolutional neural networks to apply them to transformers, in particular activation maps 
+and efficiency in a high-speed regime. Our work exploits recent findings in attention-based architectures,
+which are competitive on highly parallel processing hardware. We revisit principles from the extensive
+literature on convolutional neural networks to apply them to transformers, in particular activation maps
 with decreasing resolutions. We also introduce the attention bias, a new way to integrate positional information
-in vision transformers. As a result, we propose LeVIT: a hybrid neural network for fast inference image classification. 
-We consider different measures of efficiency on different hardware platforms, so as to best reflect a wide range of 
-application scenarios. Our extensive experiments empirically validate our technical choices and show they are suitable 
-to most architectures. Overall, LeViT significantly outperforms existing convnets and vision transformers with respect 
+in vision transformers. As a result, we propose LeVIT: a hybrid neural network for fast inference image classification.
+We consider different measures of efficiency on different hardware platforms, so as to best reflect a wide range of
+application scenarios. Our extensive experiments empirically validate our technical choices and show they are suitable
+to most architectures. Overall, LeViT significantly outperforms existing convnets and vision transformers with respect
 to the speed/accuracy tradeoff. For example, at 80% ImageNet top-1 accuracy, LeViT is 5 times faster than EfficientNet on CPU. *
 
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/levit_architecture.png"
-alt="drawing" width="600"/> 
+alt="drawing" width="600"/>
 
 <small> LeViT Architecture. Taken from the <a href="https://arxiv.org/abs/2104.01136">original paper</a>.</small>
 
@@ -38,25 +38,25 @@ Tips:
 
 - Compared to ViT, LeViT models use an additional distillation head to effectively learn from a teacher (which, in the LeViT paper, is a ResNet like-model). The distillation head is learned through backpropagation under supervision of a ResNet like-model. They also draw inspiration from convolution neural networks to use activation maps with decreasing resolutions to increase the efficiency.
 - There are 2 ways to fine-tune distilled models, either (1) in a classic way, by only placing a prediction head on top
-  of the final hidden state and not using the distillation head, or (2) by placing both a prediction head and distillation 
-  head on top of the final hidden state. In that case, the prediction head is trained using regular cross-entropy between 
-  the prediction of the head and the ground-truth label, while the distillation prediction head is trained using hard distillation 
-  (cross-entropy between the prediction of the distillation head and the label predicted by the teacher). At inference time, 
-  one takes the average prediction between both heads as final prediction. (2) is also called "fine-tuning with distillation", 
-  because one relies on a teacher that has already been fine-tuned on the downstream dataset. In terms of models, (1) corresponds 
+  of the final hidden state and not using the distillation head, or (2) by placing both a prediction head and distillation
+  head on top of the final hidden state. In that case, the prediction head is trained using regular cross-entropy between
+  the prediction of the head and the ground-truth label, while the distillation prediction head is trained using hard distillation
+  (cross-entropy between the prediction of the distillation head and the label predicted by the teacher). At inference time,
+  one takes the average prediction between both heads as final prediction. (2) is also called "fine-tuning with distillation",
+  because one relies on a teacher that has already been fine-tuned on the downstream dataset. In terms of models, (1) corresponds
   to [`LevitForImageClassification`] and (2) corresponds to [`LevitForImageClassificationWithTeacher`].
-- All released checkpoints were pre-trained and fine-tuned on  [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k) 
+- All released checkpoints were pre-trained and fine-tuned on  [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k)
   (also referred to as ILSVRC 2012, a collection of 1.3 million images and 1,000 classes). only. No external data was used. This is in
   contrast with the original ViT model, which used external data like the JFT-300M dataset/Imagenet-21k for
   pre-training.
-- The authors of LeViT released 5 trained LeViT models, which you can directly plug into [`LevitModel`] or [`LevitForImageClassification`]. 
+- The authors of LeViT released 5 trained LeViT models, which you can directly plug into [`LevitModel`] or [`LevitForImageClassification`].
   Techniques like data augmentation, optimization, and regularization were used in order to simulate training on a much larger dataset
   (while only using ImageNet-1k for pre-training). The 5 variants available are (all trained on images of size 224x224):
   *facebook/levit-128S*, *facebook/levit-128*, *facebook/levit-192*, *facebook/levit-256* and
   *facebook/levit-384*. Note that one should use [`LevitFeatureExtractor`] in order to
   prepare images for the model.
 - [`LevitForImageClassificationWithTeacher`] currently supports only inference and not training or fine-tuning.
-- You can check out demo notebooks regarding inference as well as fine-tuning on custom data [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/VisionTransformer) 
+- You can check out demo notebooks regarding inference as well as fine-tuning on custom data [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/VisionTransformer)
   (you can just replace [`ViTFeatureExtractor`] by [`LevitFeatureExtractor`] and [`ViTForImageClassification`] by [`LevitForImageClassification`] or [`LevitForImageClassificationWithTeacher`]).
 
 This model was contributed by [anugunj](https://huggingface.co/anugunj). The original code can be found [here](https://github.com/facebookresearch/LeViT).
@@ -71,6 +71,12 @@ This model was contributed by [anugunj](https://huggingface.co/anugunj). The ori
 [[autodoc]] LevitFeatureExtractor
     - __call__
 
+## LevitImageProcessor
+
+  [[autodoc]] LevitImageProcessor
+    - preprocess
+
+
 ## LevitModel
 
 [[autodoc]] LevitModel